The Rhetorical Structure of Modus Tollens: An Exploration in Logic-Mining

A general method for mining discourse for occurrences of the rules of inference would be useful in a variety of natural language processing applications. The method described here has its roots in Rhetorical Structure Theory (RST). An RST analysis of a rule of inference can be used as an exemplar to produce a relational complex in the form of a nested relational proposition. This relational complex can be transformed into a logical expression using the logic of relational propositions. The expression can then be generalized as a logical signature for use in logic-mining discourse for instances of the rule. Generalized logical signatures reached in this manner appear to be grounded in identifiable logical relationships with their respective rules of inference. Thus, from a text, it is possible to identify a rhetorical structure, and from the structure, a relational proposition, and from the relational proposition, a generalized logical signature, and from the signature, the rule of inference residing within the text. The focus in this paper is on modus tollens and its variants, but the method is extensible to other rules as well

Identifying Causal Structures from Cyberstalking: Behaviors Severity and Association

This paper presents an etiological cyberstalking study, meaning the use of various technologies and internet in general to harass or to stalk someone. The novelty of the paper is the multivariate empirical approach of cyberstalking victimization that has received less attention from the research community. Also, there is a lack of such studies from the causal perspective. It happens, since in most of the studies, a priority is given on a single causation identification, whereas the data examination used for mining causal relationships in this paper presents a novel and great potential to detect combined or multiple cause factors. The paper focuses in the impact that variables such as age, gender and the fact whether the participant has ever harassed someone, is related to the fact of being victim of cyberstalking. The research aims to find the causes of cyberstalking in high school’s teenagers. Furthermore, an exploratory data analysis has been performed. A weak and moderate correlation between the factors on the dataset is emphasized. The odds ratio among the variables has been calculated, which implies that girls are twice as likely as boys to be cyberstalked. Similarly, concerning outcomes related to cyberstalking frequency recidivism are noticed

Belief Evolution Network-based Probability Transformation and Fusion

Smets proposes the Pignistic Probability Transformation (PPT) as the decision layer in the Transferable Belief Model (TBM), which argues when there is no more information, we have to make a decision using a Probability Mass Function (PMF). In this paper, the Belief Evolution Network (BEN) and the full causality function are proposed by introducing causality in Hierarchical Hypothesis Space (HHS). Based on BEN, we interpret the PPT from an information fusion view and propose a new Probability Transformation (PT) method called Full Causality Probability Transformation (FCPT), which has better performance under Bi-Criteria evaluation. Besides, we heuristically propose a new probability fusion method based on FCPT. Compared with Dempster Rule of Combination (DRC), the proposed method has more reasonable result when fusing same evidence

Naval Integration into Joint Data Strategies and Architectures in JADC2

NPS NRP Technical ReportAs Joint capabilities mature and shape into the Joint All Domain C2 Concept, Services, COCOMs and Coalition Partners will need to invest into efforts that would seamlessly integrate into Joint capabilities. The objective for the Navy is to study the options for Navy, including Naval Special Warfare Command under SOCOM, on how to integrate Navy's data strategy and architecture under the unifying JADC2 umbrella. The other objectives are to explore alternatives considered by the SOCOM and the Air Force, which are responsible for JADC2 Information Advantage and Digital Mission Command & Control. A major purpose of Joint, Services/COCOMs, agencies and Coalition Partners capabilities is to provide shared core of integrated canonical services for data, information, and knowledge with representations for vertical interoperability across all command levels and JADC2, lateral interoperability between Naval Service/COCOMs, and any combination of JADC2 constituents, agencies, and coalition partners. Our research plan is to explore available data strategy options by leveraging previous NRP work (NPS-20-N313-A). We will participate in emerging data strategy by Navy JADC2 project Overmatch. By working with MITRE our team will explore Air Force JADC2 data strategy implemented in ABMS DataOne component. Our goal is to find a seamless integration between Naval Data Strategy and data strategies behind JADC2 Information Advantage and Digital Mission Command & Control capabilities. Our plan includes studying Service-to-Service and Service-to-COCOM interoperability options required for Joint operations with a goal to minimize OODA's loop latency across sensing, situation discovery & monitoring, and knowledge understanding-for-planning, deciding, and acting. Our team realizes JADC2 requires virtual model allowing interoperability between subordinate C2 for services, agencies, and partner. Without such flexible 'joint' intersection organizational principal hierarchical structure it would be impossible to define necessary temporal and spatial fidelities for each level of organizational command required for implanting JADC2. Research deliverables will document the results of the exploration of Joint, COCOM, Agency and Partner Data Strategies approaches as JADC2 interoperability options to the emerging JADC2. We strive for standard JADC2 interface. Keywords: JADC2, ABMS, DataOne, Information Advantage, Digital Mission Command, IntegrationN2/N6 - Information WarfareThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

A New Evolutionary Algorithm For Mining Noisy, Epistatic, Geospatial Survey Data Associated With Chagas Disease

The scientific community is just beginning to understand some of the profound affects that feature interactions and heterogeneity have on natural systems. Despite the belief that these nonlinear and heterogeneous interactions exist across numerous real-world systems (e.g., from the development of personalized drug therapies to market predictions of consumer behaviors), the tools for analysis have not kept pace. This research was motivated by the desire to mine data from large socioeconomic surveys aimed at identifying the drivers of household infestation by a Triatomine insect that transmits the life-threatening Chagas disease. To decrease the risk of transmission, our colleagues at the laboratory of applied entomology and parasitology have implemented mitigation strategies (known as Ecohealth interventions); however, limited resources necessitate the search for better risk models. Mining these complex Chagas survey data for potential predictive features is challenging due to imbalanced class outcomes, missing data, heterogeneity, and the non-independence of some features. We develop an evolutionary algorithm (EA) to identify feature interactions in Big Datasets with desired categorical outcomes (e.g., disease or infestation). The method is non-parametric and uses the hypergeometric PMF as a fitness function to tackle challenges associated with using p-values in Big Data (e.g., p-values decrease inversely with the size of the dataset). To demonstrate the EA effectiveness, we first test the algorithm on three benchmark datasets. These include two classic Boolean classifier problems: (1) the majority-on problem and (2) the multiplexer problem, as well as (3) a simulated single nucleotide polymorphism (SNP) disease dataset. Next, we apply the EA to real-world Chagas Disease survey data and successfully archived numerous high-order feature interactions associated with infestation that would not have been discovered using traditional statistics. These feature interactions are also explored using network analysis. The spatial autocorrelation of the genetic data (SNPs of Triatoma dimidiata) was captured using geostatistics. Specifically, a modified semivariogram analysis was performed to characterize the SNP data and help elucidate the movement of the vector within two villages. For both villages, the SNP information showed strong spatial autocorrelation albeit with different geostatistical characteristics (sills, ranges, and nuggets). These metrics were leveraged to create risk maps that suggest the more forested village had a sylvatic source of infestation, while the other village had a domestic/peridomestic source. This initial exploration into using Big Data to analyze disease risk shows that novel and modified existing statistical tools can improve the assessment of risk on a fine-scale

Что и как спрашивают в социальных вопросно-ответных сервисах по-русски?

In our study we surveyed different approaches to the study of questions in traditional linguistics, question answering (QA), and, recently, in community question answering (CQA). We adapted a functional-semantic classification scheme for CQA data and manually labeled 2,000 questions in Russian originating from [email protected] CQA service. About half of them are purely conversational and do not aim at obtaining actual information. In the subset of meaningful questions the major classes are requests for recommendations, or how-questions, and fact-seeking questions. The data demonstrate a variety of interrogative sentences as well as a host of formally non-interrogative expressions with the meaning of questions and requests. The observations can be of interest both for linguistics and for practical applications

Temporospatial Context-Aware Vehicular Crash Risk Prediction

With the demand for more vehicles increasing, road safety is becoming a growing concern. Traffic collisions take many lives and cost billions of dollars in losses. This explains the growing interest of governments, academic institutions and companies in road safety. The vastness and availability of road accident data has provided new opportunities for gaining a better understanding of accident risk factors and for developing more effective accident prediction and prevention regimes. Much of the empirical research on road safety and accident analysis utilizes statistical models which capture limited aspects of crashes. On the other hand, data mining has recently gained interest as a reliable approach for investigating road-accident data and for providing predictive insights. While some risk factors contribute more frequently in the occurrence of a road accident, the importance of driver behavior, temporospatial factors, and real-time traffic dynamics have been underestimated. This study proposes a framework for predicting crash risk based on historical accident data. The proposed framework incorporates machine learning and data analytics techniques to identify driving patterns and other risk factors associated with potential vehicle crashes. These techniques include clustering, association rule mining, information fusion, and Bayesian networks. Swarm intelligence based association rule mining is employed to uncover the underlying relationships and dependencies in collision databases. Data segmentation methods are employed to eliminate the effect of dependent variables. Extracted rules can be used along with real-time mobility to predict crashes and their severity in real-time. The national collision database of Canada (NCDB) is used in this research to generate association rules with crash risk oriented subsequents, and to compare the performance of the swarm intelligence based approach with that of other association rule miners. Many industry-demanding datasets, including road-accident datasets, are deficient in descriptive factors. This is a significant barrier for uncovering meaningful risk factor relationships. To resolve this issue, this study proposes a knwoledgebase approximation framework to enhance the crash risk analysis by integrating pieces of evidence discovered from disparate datasets capturing different aspects of mobility. Dempster-Shafer theory is utilized as a key element of this knowledgebase approximation. This method can integrate association rules with acceptable accuracy under certain circumstances that are discussed in this thesis. The proposed framework is tested on the lymphography dataset and the road-accident database of the Great Britain. The derived insights are then used as the basis for constructing a Bayesian network that can estimate crash likelihood and risk levels so as to warn drivers and prevent accidents in real-time. This Bayesian network approach offers a way to implement a naturalistic driving analysis process for predicting traffic collision risk based on the findings from the data-driven model. A traffic incident detection and localization method is also proposed as a component of the risk analysis model. Detecting and localizing traffic incidents enables timely response to accidents and facilitates effective and efficient traffic flow management. The results obtained from the experimental work conducted on this component is indicative of the capability of our Dempster-Shafer data-fusion-based incident detection method in overcoming the challenges arising from erroneous and noisy sensor readings

The Inhuman Overhang: On Differential Heterogenesis and Multi-Scalar Modeling

As a philosophical paradigm, differential heterogenesis offers us a novel descriptive vantage with which to inscribe Deleuze’s virtuality within the terrain of “differential becoming,” conjugating “pure saliences” so as to parse economies, microhistories, insurgencies, and epistemological evolutionary processes that can be conceived of independently from their representational form. Unlike Gestalt theory’s oppositional constructions, the advantage of this aperture is that it posits a dynamic context to both media and its analysis, rendering them functionally tractable and set in relation to other objects, rather than as sedentary identities. Surveying the genealogy of differential heterogenesis with particular interest in the legacy of Lautman’s dialectic, I make the case for a reading of the Deleuzean virtual that departs from an event-oriented approach, galvanizing Sarti and Citti’s dynamic a priori vis-à-vis Deleuze’s philosophy of difference. Specifically, I posit differential heterogenesis as frame with which to examine our contemporaneous epistemic shift as it relates to multi-scalar computational modeling while paying particular attention to neuro-inferential modes of inductive learning and homologous cognitive architecture. Carving a bricolage between Mark Wilson’s work on the “greediness of scales” and Deleuze’s “scales of reality”, this project threads between static ecologies and active externalism vis-à-vis endocentric frames of reference and syntactical scaffolding